Orac Lies Again, Part 2

Mouse avatars are yet another example of the
animal model community admitting that animal models are not predictive yet,
but that this one might be the answer. They are asking society to continue
to fund the game, as this one just might be the winner. “Step right up,
young man and win one for the little lady. Today could be your lucky day!”
In reality, the game is rigged.

In part I, I discussed yet again what the word predict means in science
and how it can be used in two different ways. In this blog, I will address
the specific use of mice to predict human response as Dr Gorski discusses in
his essay. Mouse avatars are mice that have been implanted with human
cancers and are treated simultaneously with the patients from whom the
cancers have been removed. The thinking being that if the treatment does or
does not work in the patient, the mouse can be studied in an attempt to
explain why as well as functioning as a test subject for potential future
treatments. This is a good example of using animals as predictive models for
human response to drugs. Indeed, the NY Times article discussed by Dr
Gorski, Seeking Cures, Patients Enlist Mice Stand-Ins quotes Colin Collins,
a professor at the University of British Columbia, as saying: “The mice
allow you the opportunity to test drugs to find out which ones will be
efficacious without exposing the patient to toxicity.” That's pretty
straightforward.

Dr Gorski analyzes the concept and the current implementation of the
concept and concludes: “I do think that sequencing cancer genomes and doing
expression profiling, then using mouse models like this, could hold
considerable promise for predicting individual patient response to different
regimens.” In fairness, Dr Gorski was critical of many aspects of the
practice, but as the above indicates, he thinks it has potential. Dr Gorski
continues: “So in the end, what we have here is an animal model that very
well might be predictive of human response in a way that is more direct and
individualized.” Again, this is stated with caveats, but his position is
clearly that such models might prove to be predictive modalities. I will now
explain where and why I disagree with Dr Gorski’s position.

After someone understands the prediction problem in animal modelling,
based on a survey of the empirical evidence from many fields, invariably he
will grant that animal models so far have failed as predictive modalities
but insist that this does not mean they will always fail. And you know what?
He is right!

If one looks at a great amount of empirical evidence from fields as
diverse as toxicity testing, bioavailability testing, cancer research,
HIV/AIDS research, stroke research, and so forth one can make a definitive
case for the fact that animal models have failed to function as predictive
modalities for human response to drugs and disease, the occasional
correlation notwithstanding. The phrase “for human response to drugs and
disease” is key here, as I have pointed out many times that animal models
can be predictive for response to perturbations that occur at lower levels
of organization, where complex systems can be described in terms of simple
systems. For example, tossing a monkey out of a plane at 10,000 feet above
ground level will be predictive for the outcome for tossing a human out at
that altitude. Splat! That is not the same level of organization in a
complex system as where drug and disease response occurs. For more, see
Animal models and conserved processes. (An understanding of complexity
science is essential to understanding my position. If you cannot list at
least seven characteristics of a complex system, you don’t know what you are
talking about and need to put in some time studying before commenting on all
this.)

So the empirical evidence only gets you so far when debunking the animal
model as a predictive modality. Just because it has not worked up until now
does not mean it will never work. Maybe we can tweak the system and make an
animal model that will predict human response to drugs and disease.
Historically, anti-vivisectionists cited examples where animals and human
demonstrated very different responses to the same drug or disease and
claimed the animal model per se was not doing what scientists claimed it was
doing. Scientists, at least the more honest ones, acknowledged that such was
indeed the case but promised that further research would allow them to
invent a model that functioned as a predictive modality and hence their
research efforts should continue to be funded. After all, if they ever did
come up with a model that had a high PPV and NPV for say teratogenicity or
carcinogenicity, all the failures would be worth it. While some may not buy
that line of reasoning there is nothing obviously wrong with it from a
science or logic perspective.

I now must digress and discuss some basic principles in philosophy of
science.

Nonscientists, and even scientists, usually do not understand the
definitions of very basic concepts of science. For example, theory, law, and
hypothesis are routinely confused. Williams [1] discovered that, of the
graduates in science that Williams surveyed:

76% equated a fact with 'truth' and 'proven'

23% defined a theory as 'unproven ideas' with less than half (47%)
recognizing a theory as a well evidenced exposition of a natural
phenomenon

34% defined a law as a rule not to be broken, and forty-one percent
defined it as an idea that science fully supports.

Definitions of 'hypothesis' were the most consistent, with 61%
recognizing the predictive, testable nature of hypotheses.

Williams states that the students did not understand the differences
between laws, theories, and facts and further did not appreciate the
difference between a scientific theory and hypothesis. Some thought
hypothesis and theory were the same thing. [1] As these definitions are
relevant to my position I will pursue the topic for a few more paragraphs.
The National Academy of Sciences (USA), explains theory as follows:

In everyday usage, “theory” often refers to a hunch
or a speculation. When people say, “I have a theory about why that
happened,” they are often drawing a conclusion based on fragmentary or
inconclusive evidence. The formal scientific definition of theory is quite
different from the everyday meaning of the word. It refers to a
comprehensive explanation of some aspect of nature that is supported by a
vast body of evidence. Many scientific theories are so well established that
no new evidence is likely to alter them substantially. . . . One of the most
useful properties of scientific theories is that they can be used to make
predictions about natural events or phenomena that have not yet been
observed. [[2] p11]

Examples of theories in science include:

The Big Bang Theory

Cell Theory

The Theory of Evolution

Atomic Theory

Kinetic Theory of Gases

The Germ Theory

Chaos Theory

Theory of Special Relativity

Theory of General Relativity

Before defining hypothesis I should contrast and compare laws of science
with theories. Laws are similar to theories in that both can be used to
predict outcomes and both have dramatic quantities and quality of evidence
to support them. Laws tend to be confined to specific situations while
theories usually include explanations in addition to predictions. Theories
tend to answer how and why questions whereas laws simply predict outcomes or
behavior. Laws of science include:

Newton’s three laws of motion

Boyle’s law

Law of conservation of energy

Joule’s first and second law

The four laws of thermodynamics

Hypothesis on the other hand, refers to ideas for explaining natural
phenomena that have not been tested or that have been inadequately tested.
Hypotheses are works in progress. They may turn out to be true or they may
not. Hypotheses may come from observations, experiments, or simply from
thinking about a problem. One should say: “I have a hypothesis that X is
correlated to Y;” not: “I have a theory that X is correlated to Y.”

So, why is the above important to this examination of mouse avatars?

Whereas anti-vivisectionists had historically criticized animal models on
ethical grounds and scientifically based on examples, Niall Shanks and Hugh
LaFollette began writing in the early 1990s about more general concerns
based on the theory of evolution and chaos and complexity theory. Shanks and
I expanded on this in our books and articles. I would not suggest that
Shanks et al came up with a new theory in science. I would say that we
combined parts of two existing theories and discovered why animal models
will always fail as predictive modalities at the level of organization
relevant to drug and disease response. Whether one wishes to classify this
as a new scientific theory, “Modelling evolved complex systems theory,”
(which I do not) or just an application of two old theories—complexity and
evolution—is not relevant to this discussion. The point is that scientific
theory regarding animal models does exist and it does exactly what a
scientific theory is supposed to do: make sense of findings from disparate
fields and “make predictions about natural events or phenomena that have not
yet been observed.”

The fact that animals and humans are examples of evolved complex systems
means that merely tweaking the animal model will not be sufficient to make
it into a predictive modality. Systems that are complex are by definition
more than the sum of their parts and hence tweaking will produce something
that is still differently complex from humans. The contribution of Shanks et
al is placing the empirical evidence, which is abundant, in the context of
the theory pertaining to evolved, complex systems.

If an animal model was invented tomorrow that showed a high PPV and NPV
for a disease or drug, evolved complex systems theory predicts that the
level of organization where the response is acting is either low enough to
be considered a simple system or occurring in a module that is the exception
to the rule. In other words the module is simulable even though it is a
complex system. Or it could be just random luck—coincidence. This means that
if such a model were invented it would not be a violation of a scientific
law but rather the exception to a scientific theory. Exceptions to theories
are very rare (some germs cause disease in one person but not another) and
when found are usually explained by the presence of other
factors—perturbations occurring at lower levels of organization, for
example. Spending money on research premised on finding exceptions to
theories in biomedical science is a fool’s errand. It also costs lives
because that money could fund other research projects.

Mouse avatars are yet another example of the animal model community
admitting that animal models are not predictive yet, but that this one might
be the answer. They are asking society to continue to fund the game, as this
one just might be the winner. “Step right up, young man and win one for the
little lady. Today could be your lucky day!” In reality, the game is rigged.

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